An investigation of influential factors of downgrade truck crashes: A logistic regression approach

Moomen, Milhan; Rezapour, Mahdi; Ksaibati, Khaled

doi:10.1016/j.jtte.2018.03.005

Cited by 40 publications

(27 citation statements)

References 9 publications

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“…The Hosmer and Lemeshow test was applied to assess goodness of fit of these logistic regression models, which uses a Pearson test statistics to compare the observed and the fitted counts [42,43]. The null hypothesis H 0 (the model provides a good fit) and alternative hypothesis H 1 (the model does not fit the data) were tested respectively.…”

Section: Results Of Binary Logistic Regression Analysis Assessment Ofmentioning

confidence: 99%

“…It shows that the prediction ability of these five models was good. Furthermore, the ROC curve was applied to evaluate the predictive power, which is a sensitivity plot against 1-specificity for all possible thresholds [42]. As an accepted performance metric, the higher area under the ROC represents the model's better predictive capacity.…”

Section: Validation Of Predicted Probabilitiesmentioning

confidence: 99%

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Demand for community-based care services and its influencing factors among the elderly in affordable housing communities: a case study in Nanjing City

Yuan

et al. 2020

BMC Health Serv Res

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Background: Community-based care services refers to the professional services provided at home to the elderly with formally assessed demands. The growth of the elderly population has increased the demand for these services, and this issue is even worse in the affordable housing community (AHC) of China. Understanding of elderly's demands for different types of community-based care services and its determinations would enable the implementation of appropriate incentive schemes to promote utilization of community-based care services in the AHCs of China. Methods: Guided by previous studies, a conceptual framework was developed. Then, a questionnaire was designed and a community based survey was conducted from May 10-20, 2018 in Daishan AHC of Nanjing City, China. Four hundred eight participants from 25,650 elderly people were selected by systematic random sampling technique. Binary logistic regression was applied to the data about the elderly' primary demands for community-based care services in the AHC, to quantify the elderly's demands and explore related individual-level factors. Results: The finding indicates that more than 50% of respondents had the demand for an elderly care hotline, building health archives, on-call nursing and doctor visits, medical lectures, regular medical examinations and sporting fitness. The binary logistic regression models revealed that the primary demands of the elderly for community-based care services were influenced by distinct factors. Conclusions: Our findings help clarify different types of community-based care services and provide fresh information about the demand for community-based care among the elderly in AHCs. Several policy implications are discussed to enhance the efficiency of community-based care service provision.

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Section: Results Of Binary Logistic Regression Analysis Assessment Ofmentioning

confidence: 99%

Section: Validation Of Predicted Probabilitiesmentioning

confidence: 99%

Demand for community-based care services and its influencing factors among the elderly in affordable housing communities: a case study in Nanjing City

Yuan

et al. 2020

BMC Health Serv Res

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“…( Manan et al, 2013;Law, 2015;Moomen et al, 2018), and the road environment (Ha and Thill., 2011;Chen et al, 2011) have been profoundly proven to have a strong correlation with traffic accidents. Apart from studies that aim to explore influential factors at a microscopic level, some other researchers have investigated how macroscopic socio-economic conditions affect traffic accidents, such as the Gross Domestic Product (GDP) growth (Bener et al, 2011;Yannis et al, 2014), the population and vehicle ownership (Van et al, 2004;Kopits et al, 2005;Garg et al, 2006;Ziyab et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model

Jiang

Liu

et al. 2020

Transportmetrica A: Transport Science

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“…A wide variety of exposure variables were described in the traffic crash models in the previous studies. All the factors can be divided into five parts: (1) human related factors, including age, fatigue driving, and drunk driving [8,9]; (2) vehicle factors, especially different types of vehicle and nonvehicle [10,11]; (3) road factors, including geometric design features [12], number of lanes at road, number of intersections, and road density; (4) environment factors, including traffic characteristics, land use type [4], and weather condition. Meanwhile, socioeconomic variables, such as population, employment, and household income, were reported to be connected with the frequency of traffic crashes.…”

Section: Safety Covariatesmentioning

confidence: 99%

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

Wang

Chen

Huang

et al. 2019

Mathematical Problems in Engineering

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This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran's I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

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An investigation of influential factors of downgrade truck crashes: A logistic regression approach

Cited by 40 publications

References 9 publications

Demand for community-based care services and its influencing factors among the elderly in affordable housing communities: a case study in Nanjing City

Demand for community-based care services and its influencing factors among the elderly in affordable housing communities: a case study in Nanjing City

Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

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